000			04331nam a22003615i 4500
999			_c88180 _d88180 _x1
001			88180
003			ES-MaUEC
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006			a||||fo|||| 00| 0
007			cr nn 008mamaa
008			161226s2016 gw | s |||| 0|eng d
020			_a9783319457970
024	7		_a10.1007/978-3-319-45797-0 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9 .A43 _b2016 EB
100	1		_aSteele, Brian _9101694 _eautor
245	1	0	_aAlgorithms for Data Science _cBrian Steele, John Chandler, Swarna Reddy
264		1	_aCham _bSpringer _c2016
300			_a1 recurso en línea (XXIII, 430 páginas) _b48 ilustraciones, 30 ilustraciones en color
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
505	0		_aIntroduction -- Data Mapping and Data Dictionaries -- Scalable Algorithms and Associative Statistics -- Hadoop and MapReduce -- Data Visualization -- Linear Regression Methods -- Healthcare Analytics -- Cluster Analysis -- k-Nearest Neighbor Prediction Functions -- The Multinomial Naive Bayes Prediction Function -- Forecasting -- Real-time Analytics.
520			_aThis textbook on practical data analytics unites fundamental principles, algorithms, and data. Algorithms are the keystone of data analytics and the focal point of this textbook. Clear and intuitive explanations of the mathematical and statistical foundations make the algorithms transparent. But practical data analytics requires more than just the foundations. Problems and data are enormously variable and only the most elementary of algorithms can be used without modification. Programming fluency and experience with real and challenging data is indispensable and so the reader is immersed in Python and R and real data analysis. By the end of the book, the reader will have gained the ability to adapt algorithms to new problems and carry out innovative analyses. This book has three parts: (a) Data Reduction: Begins with the concepts of data reduction, data maps, and information extraction. The second chapter introduces associative statistics, the mathematical foundation of scalable algorithms and distributed computing. Practical aspects of distributed computing is the subject of the Hadoop and MapReduce chapter. (b) Extracting Information from Data: Linear regression and data visualization are the principal topics of Part II. The authors dedicate a chapter to the critical domain of Healthcare Analytics for an extended example of practical data analytics. The algorithms and analytics will be of much interest to practitioners interested in utilizing the large and unwieldly data sets of the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System. (c) Predictive Analytics Two foundational and widely used algorithms, k-nearest neighbors and naive Bayes, are developed in detail. A chapter is dedicated to forecasting. The last chapter focuses on streaming data and uses publicly accessible data streams originating from the Twitter API and the NASDAQ stock market in the tutorials. This book is intended for a one- or two-semester course in data analytics for upper-division undergraduate and graduate students in mathematics, statistics, and computer science. The prerequisites are kept low, and students with one or two courses in probability or statistics, an exposure to vectors and matrices, and a programming course will have no difficulty. The core material of every chapter is accessible to all with these prerequisites. The chapters often expand at the close with innovations of interest to practitioners of data science. Each chapter includes exercises of varying levels of difficulty. The text is eminently suitable for self-study and an exceptional resource for practitioners.
988			_aEBOOK, EBSPRINGER_2016
650		7	_aAlgoritmos computacionales _2embne _9151819
650		7	_aData mining _2embne _9162648
700	1		_aChandler, John _930706 _eautor
700			_aReddy, Swarna. _9101695 _eautor
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-3-319-45797-0 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b10/2020 _dz _eo